In the past, a typical application for copiers or scan-to-print image processing systems was to reproduce an input image as accurately as possible, i.e., render a copy. Thus, copies have been rendered as accurately as possible, flaws and all. However, as customers become more knowledgeable in their document reproduction requirements, they recognize that an exact copy is often not what they want. Instead, they would rather obtain the best possible document output. Until recently, image quality from the output of a copier or a scan-to-print system was directly related to the input document quality. One very common set of input documents includes photographs. Unfortunately, photography is an inexact science, particularly among amateurs, and original photographs are often poor. Alternately, technology, age or image degradation variations often result in pictures having an unsatisfactory and undesirable appearance. What is desired then, is a copy giving the best possible picture, and not necessarily a copy of the original.
Photography has long dealt with this issue. Analog filters and illumination variations can improve the appearance of pictures in the analog photographic process. Thus, for example, yellow filters enhance the appearance of white clouds against a blue sky in black and white images. Further, various electrophotographic devices, including digital copiers, can clean up and improve images by adjustment of threshold, filtering, or background suppression. Generally, these methods are manual methods which a user must select on an image by image basis. Unfortunately, the casual user is not skilled enough to perform these operations. The inability to perform image enhancement operations is exacerbated when additionally dealing with color controls.
Three possible choices are presented by the art in the area of image enhancement. In the first case, we can do nothing. Such a system is a stable system, in that it does no harm to an image, but its output documents are sometimes not satisfactory to the ultimate customer. This is a common approach taken to reproduction.
In a second case of image enhancement, the image can always be processed. It turns out than an improvement can usually be made to an image if certain assumptions are made that are accurate for most cases. In an exceptionally large set of images, increasing contrast, sharpness, and/or color saturation, will improve the image. This model tends to produce better images, but the process is unstable, in that for multi-generation copying, increases in contrast, saturation, or sharpness are undesirable and ultimately lead to a severe image degradation. Further the process may undesirably operate on those images which are good ones.
Accordingly, we arrive at our third case of image enhancement, a process of automated image enhancement which operates to vary images which are not perceived as good images, but does not operate on images which do not need to be improved, thereby allowing a stable process.
One improvement that can be made to an image is to correct the sharpness of the image. A common deficiency of digitally represented images, and images in general, is that sharpness is less than would be considered "good" in terms of image appearance. This discrepancy might be caused by a slightly out of focus image scan, by a bad original photograph, or simply by the expectations and preferences of the user. While it is not proposed to measure and correct actual blur in the image, like focus and motion blur, the image can be changed to meet user expectations independently of the actual sharpness of the image. As noted above, it is also important that this function be accomplished in such a way that subsequent sharpness enhancement operations not result in further image modification.
One way of characterizing a lack of sharpness is that there is a lack of high contrast in local areas of the image. However, the maximum local contrast of the image provides some information about the lack of sharpness, and accordingly about a filter to correct lack of sharpness. Another characterization of lack of sharpness is that an image requires fine detail in some portion of the image in order to be considered sharp. Consider for a moment a comparison of finely detailed object with a high number of edges and a face. Even though a perfectly focused face lacks edges, somewhere in the image there will be some object, perhaps a reflection in the eyes, fine hair, etc, which can give a measurement of the maximum existing contrast in the image.
The references cited are herein incorporated by reference.